Data Collection

Description:

Documented procedure for standardized and efficient data collection of the process, collecting data that will be used to describe the Voice of the Process (VOP).

Objective:

Ensure data collection is complete, realistic, and practical. Many times this can be costly and resisted by those involved. Strive to minimize the costs and impact to those involved while obtaining as much accurate data in reasonable amount of time.

STEP 1: When setting up the collection system it is important to collect data only once and minimize the burden on the operators, team, and the GB/BB. Ensure to capture all the families of variation (FOV) that should be analyzed such as:

Part-Part

Shift-Shift

Operator-Operator

Machine-Machine

Form-Form

Tool-Tool, etc.

A Families of Variation diagram is shown below. Suit it to meet the needs of your DMAIC project. Only collect the sources of variation that are relevant.

Another possibility is shown below. Each one of the families will contribute to the overall process performance. The combination (not necessariy the sum) of all their variances represents the overall process variance. These are all "short" term sources that make up the "long" term variation.

Another example of an FOV tree

STEP 2: Complete a simple but comprehensive form that elaborates on the data and collection plan. This may later be used as an attachment used in the Control Plan when handing-off the project to the Process Owner.

Other items in addition to the FOV tree when completing the Data Collection Plan are:

1) SIPOC
2) CTQ Linkage
3) High Level Process Map

Items to include:

What is the question the team is trying to answer?

Metrics being measured

Sampling strategy

Sample size (may need visit Power and Sample Size)

Where observations are collected

How the observations are measured, what devices and units?

How the observations are recorded

Recording frequency

Data Classification

Pictures, screenshots, macros, other attachments, reference documents, and other helpful information.

Review the types of data and necessary sample sizes (observations) needed to create control charts and hypothesis testing coming up later in the MEASURE and ANALYZE phases. Meeting or exceeding the minimum (without be too costly) can lead to better analyis and stronger decision making.

Data collected through automated methods often have the most accuracy and bias. Even as simple as them seem to collect data it is important to clearly identify the source, system, menus, files, folders, etc that the information lies within. Any macros and adjustments and such should be written out with clear step by step instructions.

These systems if not in place, can be timely to install in addition to being expensive. However, improving a data collection system can be a very successful part of the project improvement process in itself.

Manual methods require more instructions and training. Minimize the amount of people involved to reduce risk of introducing variation. The higher level of instruction and detail provided to the data collectors and appraisers (those collecting the measurements) will reduce the variation component contributed from the measurement system. This amount of error will be quantified and examine in the Gage R & R.

This is usually inexpensive to put in place and should be suited to fit exactly to what is needed. However, it can be costly in terms of being labor intensive and prone to recording errors and troubleshooting suspicious data.

Adding videotape and recordings are excellent ways to capture data and have the advantage of replay and removes uncertainty in what actually occurred.

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